The work aims to construct a vision intelligent detection method for cigarette packet tear tape defects based on the unsupervised deep neural network to reduce the complaints of cigarette packet tear tape defects for the ZB47 packaging machine. First, the cigarette packet image acquisition hardware acquisition device at the CH turret position of the ZB47 packaging machine was designed and installed to obtain real-time high-precision small packet images. Second, the cigarette packet image was cropped at a fixed position according to the position of the tear tape, thereby reducing the effects of the environmental background of different working conditions and speeding up the detection speed. Then, the backbone network of the autoencoder-encoder structure was constructed, and the discriminator module in the generative adversarial networks was added to form the defect detection module. The loss function of the model was constructed according to the information between the images, the latent space and the features of the images. Finally, the cropped normal cigarette packet transparent paper images were used to train the constructed defect detection model, and the abnormal score threshold was obtained based on all normal cigarette packet images. In the actual verification stage, if the score of the detected image was greater than the abnormal score threshold, it is judged to be an abnormal image, and the cigarette packet removal device at the CH turret position was triggered to remove the defective cigarette packet. The test at production site showed that the proposed method could quickly and accurately detect the cigarette packet tear tape defects with an accuracy rate of 99.99%. The method can meet the dual requirements of the actual production process for detection accuracy and detection speed of cigarette packet tear tape defects.